Partitioning of the Manso Reservoir, MT, Brazil
Compartimentação do Reservatório de Manso, MT, Brasil
Valério, A.M., Stech, J.L. and Kampel, M.
Abstract: Aim: The aim of this study is the partitioning of the Manso reservoir in the state of Mato
Grosso using limnological and reflectance measures obtained in situ. Methods: Measures of chlorophyll-a
concentration were distributed using the ordinary krigeage for the water’s surface, middle and bottom
depths. The remote sensing reflectance was classified using the k-means technique. Limnological data was
added to the classified result to assist in characterizing the water in the reservoir. Results: We observed the
partitioning of the Manso Reservoir in two main regions: the river arm and the water body. Conclusions:
Using the methods described, it was also possible to observe the water stratification and the underflow of
the river Manso within the water body of the reservoir.
Keywords: reservoir, partitioning, water quality, radiometry, remote sensing.
Resumo: Objetivo: Estudar a compartimentação do reservatório de Manso no estado do Mato Grosso
utilizando medidas limnológicas e de reflêctancia de sensoriamento remoto obtidas in situ. Métodos: As
medidas de concentração de clorofila-a foram distribuidas utilizando a técnica de krigeagem ordinária
para as profundidades superfície, meio e fundo. As medidas de reflectância de sensoriamento remoto
foram classificadas através da técnica de k-médias. Ao resultado desta classificação foram adicionados os
dados limnológicos para auxiliar na caracterização da água do reservatório. Resultados: Foi observada
a compartimentalização do reservatório de Manso em duas regiões principais: braço do rio e corpo do
reservatório. Conclusões: Através dos métodos utilizados também foi possível observar a estratificação da
água e o mergulho do rio Manso no corpo de água do reservatório.
Palavras-chave: reservatório, compartimentalização, qualidade da água, radiometria,
sensoriamento remoto.
1. Introduction
Reservoir from Modern hydroelectric Power Plants
provide socio-economic benefits such as energy production
and options for recreational activities and sports, as well as
helping commercial fisheries, flood control, water supply,
and aesthetic beauty (Çamdevýren et al., 2005).
The effectiveness of management practices and conservation of water are key aspects of watersheds that supply urban
and rural cities. The natural conditions of these nascent can
be modified due to transport of sediment in suspension and
the bottom sediment in the river, which results in changes
of water quality and siltation. These processes eliminate or
hinder the flow of the river and decrease the usable life of
reservoirs (Pinto and Garcia, 2005).
Water within reservoirs has vertical and horizontal
movements and a continuous flow of water toward the dam.
These movements show temporal changes that depend on
the flow of water and differences in level that occur during the different seasons of the year (Imberg, 1985 apud
Tundisi, 1985).
Most of the nutrients and sediments found in water
come from one or two main tributaries, located a considerActa Limnol. Bras., 2009, vol. 21, no. 3, p. 293-298.
able distance from the dam. Thus, as water moves toward
the dam, three compartments can be noticed, having
distinct physical, chemical and biological characteristics.
These three compartments are the riverine, transition and
lacustrine zones (Thornton et al., 1990).
The riverine zone is relatively narrow, contains well
mixed water and brings significant amounts of particulate
material. Light penetration is also reduced. In the transition zone, sedimentation occurs with subsequent increase
in light penetration. The lacustrine zone operates similar
to lakes, having low sedimentation of inorganic particles
and sufficient penetration of light to promote primary
production. This layer can also appear stratified (Thornton
et al., 1990).
Thus, due to the continuous flow of water toward the
dam and the variation of residence time, the reservoirs can
be considered transitional systems between rivers and lakes,
with specific mechanisms of the basin dependent and uses
of the system.
In Brazil, reservoir water quality has shown, in general,
large changes in their trophic states depending how the river
Physical Limnology
Departamento de Sensoriamento Remoto, Instituto Nacional de Pesquisas Espaciais – INPE,
Av. dos Astronautas, 1.758, Jd. Granja, CEP 12227-010, São José dos Campos, SP, Brazil
e-mail: [email protected], [email protected], [email protected]
294
Valério, A.M., Stech, J.L. and Kampel, M.
basin is used. To avoid such changes integrated management and thorough understanding of reservoirs is essential
(Calijuri, 1999).
The use of remote sensing for monitoring and management of water resources is still growing (Novo, 2007).
Applying this tool to aquatic environments allows study of
water quality parameters such as the possible relationships
between the properties of a field of incident light and the
optical properties of water. The term “optical properties”
describes the components responsible for the change of
spectral response of the body of water (IOCCG, 2000).
This method can be used efficiently to prevent, observe
and monitor changes in the aquatic system (Dekker et al.,
1992).
By applying remote sensing techniques it’s possible to
evaluate the responses from disturbances introduced by
human activity and predict the impact of these actions on
the conditions of sustainability for the medium and long
term (Novo, 2005).
Within this context, the aim of this study is the partitioning of the Manso reservoir in the state of Mato Grosso
using limnological and reflectance measures obtained in
situ.
1.1. Area of study
The Reservoir of Manso is located in the State of Mato
Grosso, near to the Parque Nacional da Chapada dos
Guimarães and ~100km of capital, Cuiabá. The reservoir
is limited by following coordinates: 14° 14’-15° 20’ S and
55° 50’ 0’’ W
14° 50’ 0’’ S
N
W
E
S
16
17
55° 20’-60° 00’ W (Figure 1). The Rio Manso, where the
reservoir is located, is the main tributary of the Cuiabá
River (Balassa et al., 2004).
The reservoir has a water area of 427 km2, volume
of 7.3 km3 and the depth nearest to the dam is about
60 m. The reservoir was built between November/99 and
February/00. The region is tropical and has two distinct
seasons: dry (April-August) and rainy (September-March)
(Hyland et al., 2006).
The flooded area is mainly composed of shrubs, although
some regions have riparian zone, trees that were previously
surrounded by rivers. The trees were left in place, they are
now partially submerged with their branches exposed to the
air and slowly dying off (Figure 2). During decomposition,
the dead organic matter uses up much of the water’s dissolved oxygen, changing the water quality in the reservoir,
as well as of its biota (Hyland et al., 2006).
2. Material and Methods
Measurements were obtained from in situ remote sensing reflectance (Rrs) during the from February 29 to 02
March/08 using the following hyperspectral radiometers: an
above-water radiometer (ASD FieldSpec Hand Held) and
a subaquatic profiler (Satlantic HyperOCR Hyperspectral
Radiometer). Radiometric measurements were taken following the protocol suggested by Fougnie et al. (1999). The
FieldSpec data processing followed the methodology suggested by Kampel et al. (2009). With the Satlantic profiler,
55° 40’ 0’’ W
18
19
20
55° 30’ 0’’ W
9
8
7
55° 20’ 0’’ W
11
6
10
12
15° 0’ 0’’ S
1, 2, 3, 4, 5, 13, 14, 15
Cuiabá
Study area
0
3
6
Figure 1. Study area, the Manso Reservoir, Mato Grosso, Brazil and data obtained from the arm of the Manso River.
Acta Limnol. Bras., 2009, vol. 21, no. 3, p. 293-298.
12 km
Partitioning of the Manso Reservoir, MT, Brazil
Figure 2. Submerged trees in the Manso Reservoir with branches
exposed to the air.
limnological parameters such as temperature, chlorophyll-a
concentration (chl-a) and photosynthetically active radiation (PAR) were also estimated.
We performed 20 samples with concomitant measures
of FieldSpec and Satlantic radiometers. Measurements were
taken along the main arm of the Manso reservoir (Figure 1).
Due to technical or meteorological problems, FieldSpec
measurements were not taken at stations 11 to 16 and 18.
For the same reasons, Satlantic measurements were not obtained at stations 6 to 10. Water samples were also collected
to determine the concentration of chlorophyll-a at three
depths: surface, middle (approximately 10 m) and bottom
(20 m approximately) for stations 1-5, 16-20.
To show the horizontal distribution of the phytoplankton biomass, indexed as chl-a in the reservoir, the in situ
chl-a data was interpolated using the ordinary krigeage.
The k-means algorithm was used to perform the classification of the spectra collected with the FieldSpec radiometer, categorizing the water in the reservoir. According
to the results obtained from this analysis, temperature, PAR
and chl-a measured with the Satlantic profiler were also used
to characterize the water masses previously compartmentalized by that classification. These 3 variables were analyzed
for 2, 10 and 14 meter depths.
3. Results and Discussion
Using the method of ordinary krigeage, it was performed a spatial analysis on the chlorophyll concentration
data, estimated in laboratory. The method was applied
to each depth of sampling: surface, middle and bottom.
The analysis of these results shows the maximum relative
displacement of the concentration of chlorophyll (µg.L–1)
along the Manso Reservoir (Figure 3).
In Figure 3a, we observe that the maximum concentration of surface chlorophyll is near the river arm. Figure 3b
shows that the maximum of chl-a is in the middle of the
Acta Limnol. Bras., 2009, vol. 21, no. 3, p. 293-298.
295
water column of the reservoir just after the Manso river.
Figure 3c shows that the maximum chl-a at the bottom of
the reservoir is found in the reservoir’s body.
The displacement of the depth of the maximum chlorophyll-a concentration illustrated in Figures 4 a, b, and c
shows a dive of the river in the body of the reservoir. This
dive is due to the difference in the density of the river’s water
and the reservoir. This process is governed by temperature
and dissolved and suspended material in the water (Wetzel,
2001). As for the Manso reservoir, the river water has greater
density than the reservoir water (ρ river > ρ reservoir),
which is confirmed by Assireu et al. (2008).
Thus, the distribution of chl-a in the reservoir is not very
homogeneous. Therefore, to obtain the characterization of
the waters in the reservoir, a non-supervised classification of
Rrs was performed in the field using the k-means algorithm.
After performing the k-means classification, it is possible
to see in Figure 4 that the spectra could be separated into
two groups.
The Rrs clusters of two groups have high value in silhouette (above 0.5) indicating that these spectra are well
separated from their neighbouring clusters. The silhouette
plot displays a measure of how close each point in one
cluster is to points in the neighboring clusters. This measure
ranges from +1, indicating points that are very distant from
neighboring clusters, through 0, indicating points that are
not distinctly in one cluster or another, to -1, indicating
points that are probably assigned to the wrong cluster. Still,
in the second cluster, some Rrs presents low silhouette value
(below 0.5) indicating that there wasn’t a good separation
of these Rrs, and they are close to other clusters.
Limnological data sets measured in field using the
Satlantic profiler were gathered according to the clusters
and classification for each collection point. This method
was used to characterize the partitioning of the reservoir.
The following products were chosen: temperature (°C), PAR
(μmol.m–2s) and chl-a (µg.L–1) for depths of 2, 10 and 14 m
representing superficial, middle and bottom, respectively.
The results are shown in Tables 1, 2 and 3.
When observing the clusters generated by k-means,
note that the stations 1 to 5 (arm of the River Manso)
have different attributes than the others. At 2 m depth
(Table 1), the water has lower temperature and PAR, and
the chlorophyll concentration is higher. These patterns are
indicative of waters belonging to the Manso River, inside
the reservoir.
Observing the temperature and PAR at different depths,
we find that the temperature becomes homogeneous and
the PAR decreases as it extends into the reservoir. Here the
chlorophyll displays a reversal of values. That is, in stations
1 to 5 (arm of the river) its value is greater on the surface,
but at a depth of 14 m, a sharp drop in value occurs. Already
in the body of the reservoir the chl-a of the surface shows
296
Valério, A.M., Stech, J.L. and Kampel, M.
–14.75
a
Latitude
–14.80
–14.85
–14.90
–55,80 –55,75 –55,70 –55,65 –55,60 –55,55 –55,50 –55,45 –55,40
–14.75
b
Latitude
–14.80
–14.85
–14.90
–14.95
–55,85 –55,80 –55,75 –55,70 –55,65 –55,60 –55,55 –55,50 –55,45 –55,40 –55,35
–14.75
c
Latitude
–14.80
–14.85
–14.90
–14.95
–55,85 –55,80 –55,75 –55,70 –55,65 –55,60 –55,55 –55,50 –55,45 –55,40 –55,35
Longitude
600
550
500
450
400
350
300
250
200
150
100
50
300
280
260
240
220
200
180
160
140
120
100
80
60
40
20
0
700
650
600
550
500
450
400
350
300
250
200
150
100
50
0
Figure 3. Ordinary krigeage applied to in situ chlorophyll-a concentration (mg.L–1) at 3 different depths of the Manso Reservoir:
a) surface, b) middle (~10 m), and c) bottom (~14 m).
Table 1. Clustering classification of remote sensing reflectance
by k-means and parameters collected by an hyperspectral profiler
(Satlantic) to a depth of 2 m.
2 clusters
Cluster
1
2
0
0,5
Value
1
Figure 4. K-means applied to collected Rrs in March 2008 in
the Manso Reservoir.
Acta Limnol. Bras., 2009, vol. 21, no. 3, p. 293-298.
2 clusters
P1
P2
P3
P4
P5
P6
P7
P8
Temperature
26.63
27.14
26.98
27.52
28.39
-
PAR
3.90
9.87
26.08
35.39
61.78
-
Chl-a
456.01
198.79
337.22
185.47
540.28
-
P9
P16
P18
P19
P20
29.23
29.11
29.34
29.24
124.18
768.26
877.94
627.36
16.94
11.33
17.11
28.29
Partitioning of the Manso Reservoir, MT, Brazil
297
30
90
29
80
70
28
60
27
50
26
40
25
30
24
20
23
22
Chl-a (mg.L–1)
Temperature (°C)
10
1
5
9
13
17
21
25
29
33
37
41
0
Depth (m)
Temperature
Chl-a
Figure 5. Concentration of chlorophyll-a profile and temperature at point 18 (see text) during March/08 at Manso Reservoir. Data
obtained using an hyperspectral profiler with attached ancillary sensors (Satlantic).
Table 2. Clustering classification of remote sensing reflectance
by k-means and parameters collected by Satlantic to a depth of
10 m.
2 clusters
P1
P2
P3
P4
P5
P6
P7
P8
P9
P16
P18
P19
P20
Temperature
26.41
26.55
26.47
26.59
26.55
28.84
28.51
28.44
28.40
PAR
0.01
0.01
0.02
0.01
0.01
4.49
31.74
27.89
8.78
Chl-a
6.48
16.54
39.55
13.96
11.63
13.56
24.55
37.59
45.53
Table 3. Clustering classification of remote sensing reflectance
by k-means and parameters collected by Satlantic to a depth of
14 m.
2 clusters
P1
P2
P3
P4
P5
P6
P7
P8
P9
P16
P18
P19
P20
Temperature
26.42
26.49
26.46
26.52
26.54
27.58
27.02
26.95
26.68
PAR
0.01
0.04
0.01
0.07
0.01
0.84
1.18
1.00
0.05
Acta Limnol. Bras., 2009, vol. 21, no. 3, p. 293-298.
Chl-a
41.02
17.19
24.08
0.33
23.43
29.99
62.39
69.70
280.35
low values, but as the depth increases the chl-a increases as
well, with a peak at 14 m.
This behaviour suggests a stratification of water layers,
common in summer in reservoirs (Wetzel, 2001). This
depth of maximum chlorophyll coincides with the formation of a thermocline as illustrated in Figure 5.
4. Conclusion
By the methods used, three types of behaviour in Manso
Reservoir was observed: partitioning, stratification of the
water column, and the river diving into the body of the
reservoir.
The water of the reservoir could be partitioned into two
regions: 1) the river arm and 2) in the main body of water
itself. At the surface of the arm there is a higher chl-a, and
the penetration of light in the water column is lower due
to the presence of suspended sediments causing turbidity
of the water. Within the body of the reservoir, the chl-a is
lower and the light penetration is higher, with a lower K.
Due to the time of year in which the measurements
were taken, the water was stratified, a fact confirmed by
water sample analysis at different depths.
The dive of the river was also observed by applying the
ordinary method of krigeage.
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